Solve the Data Scientist Shortage with Machine Intelligence

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Scott_HowserIn this special guest feature, Scott Howser of Nutonian discusses how machine intelligence software enables users to automatically discover analytical models via sophisticated evolutionary algorithms without any human intervention, thus solving the data scientist shortage. Scott serves as SVP of products and marketing at Nutonian with responsibilities for product management, marketing and business development. Prior to joining Nutonian, Scott served as vice president of products and marketing at Hadapt with responsibilities for product management and marketing. Prior to Hadapt, Scott was vice president of product marketing at Vertica, an HP Company, with responsibility for product messaging, corporate branding, and establishing best practices for deployment and solutions architectures. Scott earned an M.B.A. from the University of Notre Dame, an M.S.I.S.M from Loyola University Chicago, and a bachelor’s degree from Ohio Dominican University.

Despite The Harvard Business Review hailing the data scientist as “the sexiest job of the 21st century,” data scientists still seem to be elusive unicorns in the big data forest. According to a recent McKinsey report, the U.S. alone faces a shortage of 140,000-190,000 of data scientists. As a result, we’re starting to see companies find ways to fill the gap. One is by hiring temporary data scientists—renting them to simply come in, do the job, and leave.

While the “rent-a-data-scientist” approach provides a short-term solution to the data scientist deficiency, the true value of a data scientist comes from the application of business context and a sophisticated understanding of the data, which a rented, temporary data scientist simply cannot provide. It is nearly impossible for a rented expert to absorb all the legacy data necessary to apply real domain expertise in a few weeks or months.

Data scientists are difficult to find because their value lies in not only presenting the data, but in delivering a holistic understanding, analysis, and context for the data. A true data scientist – leased or fully-employed – will certainly be able to scrub and sort through raw data to develop intelligent data models. However, without in-depth company knowledge, or the ability to search unlimited data, their conclusions will be limited to the data they decide is the most important.

Instead of “borrowing” perceived experts for a one-and-done job, companies are better served with software that automates the analytic modeling process while being able to apply it to the business. By utilizing software like machine intelligence, companies can distill all of the technical sophistication to empower business users to work with the data, and apply the necessary context and understanding to drive real innovation. Companies can improve the speed and scale of their data science initiatives by orders of magnitude not by hiring more data scientists, but by making data scientists significantly more productive, or by bestowing data scientist capabilities on their internal domain experts – the analysts.

What is machine intelligence, and how can it scale data science in your organization?

Machine intelligence is the new engine driving data science within some of the most innovative organizations in the world. It leverages a set of evolutionary algorithms to automatically build transparent predictive and analytical models from your raw data, so you can understand what’s happening in your data and why. Machine intelligence automatically searches an infinite equation space to bring the user the simplest, most accurate models possible to explain the data’s behavior without assuming any underlying structure in the data. The technology independently transforms, hypothesizes, tests, and validates in the same way as a data scientist would, but automation and scalable computation resources enable it to repeat this process hundreds of millions of times per second. The best part is that users (i.e. analysts, data scientists, etc.) can incorporate their domain expertise through shareable models to define clear and measurable action items to ensure the models align with business objectives. Think of it like a concierge: machine intelligence is a trusted advisor that suggests the best way to spend your time but accepts guidance regarding your goals and objectives.

The real value of utilizing machine intelligence over a rented data scientist lies in the technology’s ability to expose results in a way that incorporates human creativity and domain expertise. While a for-hire data scientist can analyze data and provide insight, the manual process to derive understanding from data is cumbersome and time-consuming. Business owners looking for accelerated time-to-value in data science projects require transparent solutions that deliver fast results that their analytics team can repeatedly utilize, versus having to continually seek external help. It’s time to welcome in the era of machine intelligence.


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